'Ols estimates' presentation slideshows

2. Fixed Effects Models

2. Fixed Effects Models. 2.1 Basic fixed-effects model 2.2 Exploring panel data 2.3 Estimation and inference 2.4 Model specification and diagnostics 2.5 Model extensions Appendix 2A - Least squares estimation. 2.1 Basic fixed effects model. Basic Elements

1.01k views • 43 slides

Topic4 Ordinary Least Squares

Topic4 Ordinary Least Squares. Suppose that X is a non-random variable Y is a random variable that is affected by X in a linear fashion and by the random variable e with E( e ) = 0 That is, E(Y) = b 1 + b 2 X Or, Y = b 1 + b 2 X + e. Y. Observed points.

594 views • 42 slides

Estimation of Production Functions: Fixed Effects in Panel Data

Estimation of Production Functions: Fixed Effects in Panel Data. Lecture VIII. Analysis of Covariance. Looking at a representative regression model

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Estimation of Production Functions: Fixed Effects in Panel Data

Estimation of Production Functions: Fixed Effects in Panel Data. Lecture VIII. Analysis of Covariance. Looking at a representative regression model

333 views • 25 slides

12 Autocorrelation

12 Autocorrelation. Serial Correlation exists when errors are correlated across periods -One source of serial correlation is misspecification of the model (although correctly specified models can also have autocorrelation) -Serial correlation does not make OLS biased or inconsistent

347 views • 19 slides

12 Autocorrelation

12 Autocorrelation. Serial Correlation exists when errors are correlated across periods -One source of serial correlation is misspecification of the model (although correctly specified models can also have autocorrelation) -Serial correlation does not make OLS biased or inconsistent

372 views • 19 slides

12 Autocorrelation

12 Autocorrelation. Serial Correlation exists when errors are correlated across periods -One source of serial correlation is misspecification of the model (although correctly specified models can also have autocorrelation) -Serial correlation does not make OLS biased or inconsistent

194 views • 19 slides

3.3 Omitted Variable Bias

3.3 Omitted Variable Bias. -When a valid variable is excluded, we UNDERSPECIFY THE MODEL and OLS estimates are biased -Consider the true population model:. -Assume this satisfies all 4 assumptions and that we are concerned with x 1 -if we exclude x 2 , our estimation becomes:.

1.41k views • 18 slides

Properties of Least Squares Regression Coefficients

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